Markov Chain Model for Generation of Daily Climate-Adjusted Streamflows
J.H. Stagge1 and G.E. Moglen1
1
Virginia Tech, Department of Civil and Environmental Engineering, Room 424Northern Virginia Center, Falls Church, VA 22043, PH (504) 952-3231; email:
jhstagge@vt.edu
ABSTRACT
A daily stochastic streamflow generation model is presented, which successfully
replicates statistics of the historical streamflow record and can produce climateadjusted daily time-series. A monthly climate model relates GCM-scale climate
indicators to discrete climate states in a Markov chain, which in turn controls the
parameters of the daily flow model. Daily flow is generated by a two-state
(increasing/decreasing) Markov chain model, with rising limb increments randomly
sampled from a Weibull distribution and the falling limb modeled as an exponential
recession. When applied to the Potomac river, a 38,000 km2 basin in the MidAtlantic United States, the model reproduces daily, monthly, and annual statistics of
the historical record, including extreme drought. This method can be utilized across a
wide range of water resources planning applications and offers the advantage of
parsimony, requiring only a sufficiently long historical streamflow record and largescale climate data.